The use of customised versus population‐based birthweight standards in predicting perinatal mortality
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to critically examine potential artifacts and biases underlying the use of 'customised' standards of birthweight for gestational age (GA). DESIGN: Population-based cohort study. SETTING: Sweden. POPULATION: A total of 782,303 singletons > or =28 weeks of gestation born in 1992-2001 to Nordic mothers with complete data on birthweight; GA; and maternal age, parity, height, and pre-pregnancy weight. METHODS: We compared perinatal mortality in four groups of infants based on the following classification of small for gestational age (SGA): non-SGA based on either population-based or customised standards (the reference group), SGA based on the population-based standard only, SGA based on the customised standard only, and SGA according to both standards. We used graphical methods to compare GA-specific birthweight cutoffs for SGA using the two standards and also used logistic regression to control for differences in GA and maternal pre-pregnancy body mass index (BMI) in the four groups. MAIN OUTCOME MEASURES: Perinatal mortality, including stillbirth and neonatal death. RESULTS: Customisation led to a large artifactual increase in the proportion of SGA infants born preterm. Adjustment for differences in GA and maternal BMI markedly reduced the excess risk among infants classified as SGA by customised standards only. CONCLUSION: The large increase in perinatal mortality risk among infants classified as SGA based on customised standards is largely an artifact due to inclusion of more preterm births.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".